Media content discovery and character organization techniques

ABSTRACT

Techniques for recommending media are described. A character preference function comprising a plurality of preference coefficients is accessed. A first character model comprises a first set of attribute values for the plurality of attributes of a first character. The first and second characters are associated with a first and second salience value, respectively. A second character model comprises a second set of attribute values for the plurality of attributes of a second character of the plurality of characters. A first character rating is calculated using the plurality of preference coefficients and the first set of attribute values. A second character rating of the second character is calculated using the plurality of preference coefficients with the second set of attribute values. A media rating is calculated based on the first and second salience values and the first and second character ratings. A media is recommended based on the media rating.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation of U.S. application Ser. No.14/800,020, filed on Jul. 15, 2015, which is a Continuation of U.S.application Ser. No. 14/466,882, filed on Aug. 22, 2014, which issued asU.S. Pat. No. 9,122,684 on Sep. 1, 2015, which is a Continuation of U.S.application Ser. No. 14/065,332, filed on Oct. 28, 2013, which issued asU.S. Pat. No. 8,819,031 on Aug. 26, 2014, which is a Continuation ofU.S. application Ser. No. 13/844,125, filed on Mar. 15, 2013, whichissued as U.S. Pat. No. 8,572,097 on Oct. 29, 2013, the contents ofwhich are incorporated herein by reference in their entirety.

BACKGROUND

1. Field

The present disclosure relates generally to the field of media contentdiscovery and, more particularly, to media content discovery usingcharacter decompositions.

2. Related Art

As media such as television shows and movies have become more ubiquitousand easily accessible in the everyday lives of consumers, the quantityand diversity of the media have also significantly increased.Previously, consumers were limited to a few television channelsbroadcasted by major television networks. As technology has progressed,various media are available for on-demand viewing at the convenience ofconsumers. As this on-demand ability has become more prevalent in thetelevision industry (e.g., on-demand movies) and the personal computingindustry (e.g., YouTube video streaming), consumers have becomeoverwhelmed with the availability of choices at any one time. Similarly,consumers' ability to search through media to discover new content thatmeets their personal preferences and tastes has remained inefficient andineffective.

Traditional techniques for discovering new media rely on friends andacquaintances suggesting media that they believe the consumer may enjoy.Alternatively, the consumer may see a preview for media that capturestheir attention or the consumer may view media because it includes afavorite actor or actress. However, these techniques have a significantdrawback in that they use only a very narrow degree of precision inidentifying content and can be unreliable. For example, although afavorite actress may play the role of an educated, humble, andempowering individual in one movie, the same actress may play the roleof an illiterate, ill-mannered, and unfavorable individual in asubsequent movie. Therefore, understanding the qualities of charactersis helpful for appreciating the media in which the characters appear.

Accordingly, techniques for efficiently and reliably decomposing theattributes of characters are advantageous.

BRIEF SUMMARY

Systems and processes for discovering and recommending media content aredescribed. A set of salience values for a plurality of charactersappearing in a media content are accessed. The set of salience valuesare associated with the media content. A character preference functionof a user is accessed. The character preference function comprisesinformation identifying a plurality of preference coefficients. Eachpreference coefficient of the plurality of preference coefficients isassociated with at least one attribute of interest of a plurality ofattributes. A first character model is accessed. The first charactermodel comprises information identifying a first set of attribute valuesfor the plurality of attributes of a first character of the plurality ofcharacters. The first character is associated with a first saliencevalue of the set of salience values. A second character model isaccessed. The second character model comprises information identifying asecond set of attribute values for the plurality of attributes of asecond character of the plurality of characters. The second character isassociated with a second salience value of the set of salience values. Afirst character rating of the first character is calculated byperforming a summation of the products of the plurality of preferencecoefficients with the first set of attribute values. A second characterrating of the second character is calculated by performing a summationof the products of the plurality of preference coefficients with thesecond set of attribute values. A media content rating is calculatedbased on the first salience value, the second salience value, the firstcharacter rating, and the second character rating. The media content isrecommended to the user based on the media content rating.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates character models in vector space for multiplecharacters.

FIG. 2 illustrates an exemplary block diagram for performing discoveryand organization of characters and media content,

FIG. 3 illustrates an exemplary process for recommending media.

FIG. 4 illustrates another exemplary process for recommending media.

FIG. 5 illustrates an exemplary computing system.

DETAILED DESCRIPTION

The following description is presented to enable a person of ordinaryskill in the art to make and use the various embodiments. Descriptionsof specific devices, techniques, and applications are provided only asexamples. Various modifications to the examples described herein will bereadily apparent to those of ordinary skill in the art, and the generalprinciples defined herein may be applied to other examples andapplications without departing from the spirit and scope of the variousembodiments. Thus, the various embodiments are not intended to belimited to the examples described herein and shown, but are to beaccorded the broadest scope consistent with the claims.

The embodiments described herein include technologies directed toenabling the organization and discovery of characters and media contentbased on the characters (or attributes of the characters) present in themedia. Media and media content refer to content for storing ordelivering information. For example, media content may includetelevision shows, movies, YouTube videos, digital streaming Internetvideos, books, poems, stories, audio files, advertisements, news, andthe like.

A character refers to a persona. For example, characters may includepoliticians, actors/actresses, real-world persons, book characters, andthe like. Attributes of characters refer to qualities of the characters.For example, the character's career (e.g., scientist, lawyer, doctor,secretary), demographic (e.g., age, gender, race, parental status),location (e.g., urban, rural), social traits (e.g., nice, loyal, funny,leader, popular, friendly), physical traits (e.g., tall, short, weight,attractiveness), intellectual traits (e.g., competent at a particulartask, intelligent, hardworking, good at math), life traits (e.g.,underdog, spoiled), and the like are attributes of characters.Attributes may be represented in a binary space, such as differentiatingbetween a character being “nice” or “not nice.” Attributes may also berepresented in a continuous space, such as differentiating the degree towhich a character is nice on a real number scale of 0 to 10, −10 to 10,0 to 100, or the like.

A consumer may enjoy a particular television show because of thepositive message portrayed by a character in the show. This positivemessage is often based on multiple attributes of the characters in theshow, rather than strictly the characters' actions or the content of thecharacter's verbal speech. For example, attractive female charactersthat are depicted as confident and intelligent portray a positivemessage. To understand why the consumer is attracted to a character, itis helpful to build a character model that captures the character'sattributes. A character preference function based on the consumer'spreferred attributes may also be developed by either directly elicitingwhy a particular consumer likes or relates to characters, or simply byinferring a preference function based on a set of preferred and/ornon-preferred characters. Character models and character preferencefunctions are used to recommend media to a consumer, rate the likelihoodthat a consumer will enjoy or respond to particular piece of content,recommend new characters to a consumer, recommend or deliver othercontent to the consumer, or rate the likelihood that a consumer willenjoy a particular character or piece of content.

Character Models in Vector Space

FIG. 1 illustrates character models in vector space for multiplecharacters. In FIG. 1, character models 116-128 are mapped out based onthe attributes of the characters 102-114 from the television show TheBig Bang Theory. The Big Bang Theory television show is a sitcom thatbrings together a wide array of characters ranging from geeky,intellectual characters with limited social skills to characters thatare less educated, but socially adept. Each character model illustratedin FIG. 1 captures a multidimensional representation of the attributesof an associated character. In this example, each of the charactermodels 116-128 is stored in the form of a vector. One of ordinary skillin the art will appreciate that the character models may be stored usingvarious methodologies. In the example of FIG. 1, the vectors forcharacter models 116-128 are four-dimensional. The value in the firstdimension of each character model is based on the character's gender,with female represented by 1 and male represented by a −1. The value inthe second dimension of each character model is based on whether thecharacter is a scientist, with scientist represented by 1 andnon-scientist represented by −1. The value in the third dimension ofeach character model is based on the attractiveness of the character,with −1 representing unattractive, 0 representing neutral attractive,and 1 representing attractive. The value in the fourth dimension of eachcharacter model is based on whether the character is friendly, with 1representing friendly and −1 representing unfriendly.

In The Big Bang Theory sitcom, Sheldon 102 is a male theoreticalphysicist researching quantum mechanics and string theory. Sheldon hasB.S., M.S., M.A., Ph.D., and Sc.D. degrees. He is an unfriendlyintrovert who is physically unattractive. Sheldon's 102 attributes aremapped to vector 116. Vector 116 is (−1, 1, −1, −1). Vector 116 isannotated for clarity as (−1 [male], 1 [scientist], −1 [unattractive],−1 [unfriendly]).

Leonard 104 is a male physicist on The Big Bang Theory. He received hisPh.D. at the age of 24. His physical attractiveness is neutral, meaninghe is neither attractive nor unattractive, and he is friendly. Leonard's104 attributes are mapped to vector 118. Vector 118 is (−1, 1, 0, 1).Vector 118 is annotated for clarity as (−1 [male], 1 [scientist], 0[neutral attractive], 1 [friendly]).

Penny 106 is a friendly, attractive, tall, blonde, female waitress whois pursuing a career in acting. Penny's 106 attributes are mapped tovector 120. Vector 120 is (1, −1, 1, 1). Vector 120 is annotated forclarity as (1 [female], −1 [nonscientist], 1 [attractive], 1[friendly]).

Howard 108 is a male aerospace engineer and has an M.Eng. degree. He issocially outgoing and friendly but is physically unattractive. Howard's108 attributes are mapped to vector 122. Vector 122 is (−1, 1, −1, 1).Vector 122 is annotated for clarity as (−1 [male], 1 [scientist], −1[unattractive], 1 [friendly]).

Rajesh 110 is a male particle astrophysicist at Caltech and has a Ph.D.His physical attractiveness is neutral and he is friendly. Rajesh's 110attributes are mapped to vector 124. Vector 124 is (−1, 1, 0, 1). Vector124 is annotated for clarity as (−1 [male], 1 [scientist], 0 [neutralattractive], 1 [friendly]).

Bernadette 112 is a female with a Ph.D. in microbiology. She is bothattractive and friendly. Bernadette's 112 attributes are mapped tovector 126. Vector 126 is (1, 1, 1, 1). Vector 126 is annotated forclarity as (1 [female], 1 [scientist], 1 [attractive], 1 [friendly]).

Amy 114 is a female who has a Ph.D. in neurobiology. She is unfriendlyand physically unattractive. Amy's 114 attributes are mapped to vector128. Vector 128 is (1, 1, −1, −1). Vector 128 is annotated for clarityas (1 [female], 1 [scientist], −1 [unattractive], −1 [unfriendly]).

Vector Space Driven Searches

Character models described in vector space may be used for varioussearches. In one example, character models 116-128 of FIG. 1 may be usedto quickly and accurately identify all characters that exhibit aparticular attribute. To identify all characters that are attractive, asearch is conducted where an equality test is performed on the thirdelement of the character model vectors. As discussed above and describedin the example of FIG. 1, the third dimension (or third value) of eachvector indicates the attractiveness of the associated character. Allcharacter models with a value greater than zero as the third dimensionof the vector are associated with an attractive character. In theexample of FIG. 1, Penny and Bernadette are quickly and accuratelyidentified as being attractive by determining that they have anattractiveness value that is greater than 0.

Similarly, Sheldon, Howard, and Amy can be quickly and accuratelyidentified as unattractive as they have an attractiveness value that isless than 0, indicating they are unattractive. As discussed above, thesecond dimension (or second value) of the character models 116-128describe whether the character is a scientist or nonscientist. A searchfor all scientists would identify all character models with a value of 1in the second dimension. In the example illustrated in FIG. 1, a searchfor scientists returns results for Sheldon, Leonard, Howard, Rajesh,Bernadette, and Amy—everyone except Penny.

Additionally, a search for a particular characteristic of a charactermay depend on multiple dimensions of the character model vector. Forexample, a search for a “scientist” may be conducted by identifyingcharacters with character models that identify them as both “likesscience” and “good at science.”

Vector space may also be used to determine the distance betweencharacters. This distance is representative of how related (similar ordissimilar) two characters are to each other. Several techniques may beemployed to determine the distance between two characters.

Using a first technique, the distance d between a first character {rightarrow over (x)} associated with a first character model vector (x₁, x₂,x₃, x₄) and a second character {right arrow over (y)} associated with asecond character model vector (y₁, y₂, y₃, y₄) can be determined usingthe weighted Euclidean distance:

${d( {\overset{arrow}{x},\overset{arrow}{y}} )} = \sqrt{{\beta_{1}( {x_{1} - y_{1}} )}^{2} + {\beta_{2}( {x_{2} - y_{2}} )}^{2} + {\beta_{3}( {x_{3} - y_{3}} )}^{2} + {\beta_{4}( {x_{4} - y_{4}} )}^{2}}$More generally, the weighted Euclidean distance d between a firstcharacter {right arrow over (x)} and a second character {right arrowover (y)} for an N-dimensional space can be calculated using thefollowing equation:d(x,y)=√{square root over (Σ_(i=1) ^(n)β_(i)(x _(i) −y _(i))²)}

As an example of this first technique, the distance between Sheldon andLeonard can be computed using the character models 116 and 118 of FIG. 1as the following, assuming the weights β_(i)=1 for all i:

${d( {{Sheldon},{Leonard}} )} = {\sqrt{( {( {- 1} ) - ( {- 1} )} )^{2} + ( {1 - 1} )^{2} + ( {( {- 1} ) - 0} )^{2} + ( {( {- 1} ) - 1} )^{2}} = \sqrt{5}}$As illustrated by this calculation, elements of the character modelsthat have the same value do not contribute to the distance. Thus, if twocharacters have identical character models, their distance will be 0. Inthe case of Sheldon and Leonard, they share many, but not all,attributes. In particular, the differences between Sheldon and Leonardare their attractiveness and their friendliness. The squared differencein friendliness has a larger contribution (i.e., 4) than thecontribution (i.e., 1) resulting from the squared difference inattractiveness. As a result, the distance between the two characters isthe square root of 5.

Using a second technique, the distance d between a first character{right arrow over (x)} associated with a first character model vector(x₁, x₂, x₃, x₄) and a second character {right arrow over (y)}associated with a second character model vector (y₁, y₂, y₃, y₄) can bedetermined by performing a comparison of values of the character models:d({right arrow over (x)},{right arrow over (y)})=(x ₁ !=y ₁)+(x ₂ !=y₂)+(x ₃ !=y ₃)+(x ₄ !=y ₄)In this comparison, the result of two compared values is 1 when they arenot equal. Similarly, the result of two compared values is 0 when theyare equal. If x₁ and y₁ are not equal, the value of (x₁!=y₁) will be 1.This will contribute a value of 1 to the distance d({right arrow over(x)},{right arrow over (y)}). Alternatively, if x₁ and y₁ are equal, thevalue of (x₁!=y₁) will be 0. This will not contribute to the distanced({right arrow over (x)},{right arrow over (y)}) Accordingly, distanceis less for characters using this second technique when the charactersshare attributes.

Once again, it might be true that certain axes are more important eitherin general, or to a specific user than others. This can once again berepresented by a set of “weights” β_(i). More generally, the distance dbetween a first character {right arrow over (x)} and a second character{right arrow over (y)} for an N-dimensional space can be calculatedusing the following equation:

${d( {\overset{arrow}{x},\overset{arrow}{y}} )} = {\sum\limits_{i = 1}^{n}\;{\beta_{i}( {x_{i}!=y_{i}} )}}$

As an example of this second technique, the distance between Sheldon andLeonard can be computed using the character models 116 and 118 of FIG. 1as the following:d(Sheldon,Leonard)=((−1)!=(−1))+(1!=1)+((−1)!=0)+((−1)!=1)=2As illustrated by this calculation, elements of the character modelsthat have the same value do not contribute to the distance. Thus, if twocharacters have identical character models, their distance will be 0. Inthe case of Sheldon and Leonard, they share many, but not all,attributes. In particular, the differences between Howard and Rajesh areattractiveness and friendliness. The two differences each contribute thesame amount to the distance (i.e., 1). As a result, the distance betweenthe two characters is 2.

Both of these techniques use simple, symmetric functions often used tocompute distances in vector spaces. However, in the case of charactersit may be true that when computing the distance from a first character{right arrow over (x)} to a second character {right arrow over (y)} youmay consider attributes “important” to character {right arrow over (x)}more important, while when computing the converse distance fromcharacter {right arrow over (y)} to character {right arrow over (x)} youwould consider attributes “important” to character {right arrow over(y)}. For example—we might decide that whenever a character was“neutral” on a particular attribute, the “weight” on that attribute is0, and otherwise the “weight” on that attribute should be one. In thiscase the distance from Sheldon to Leonard:

${d( {{Sheldon},{Leonard}} )} = {\sqrt{( {{1( {{- {1--}}1} )^{2}} + {1( {{- {1--}}1} )^{2}} + {1( {{- 1} - 0} )^{2}} + {1( {{- 1} - 1} )^{2}}} } = \sqrt{5}}$However,

${d( {{Leonard},{Sheldon}} )} = {\sqrt{( {{1( {{- {1--}}1} )^{2}} + {1( {{- {1--}}1} )^{2}} + {0( {{0--}1} )^{2}} + {1( {{1--}1} )^{2}}} } = 2}$Thus, the distance from Sheldon to Leonard is greater than the distancefrom Leonard to Sheldon because “appearance” is more salient toSheldon's character than Leonard's. There are many other ways in whichthese distance functions might be complicated to accommodate features ofthe character space, or of the specific user.

In full generality, any function taking two elements in the characterspace to a scalar could be used as a distance function. For distance d:d:(

^(n)×

^(n))→

In another example, both the first and second techniques for determiningdistance between two characters will result in larger distances betweenPenny and Sheldon than were computed for Sheldon and Leonard. Thedistances between Penny and Sheldon will be at their maximum for the twotechniques because Penny and Sheldon are exact opposites on all fourdimensions of their character models.

As discussed above, the distance between characters represents thedegree of similarity between the characters. Thus, when it is known thata consumer likes a particular character, a computing system canrecommend additional characters that have a relatively low distance fromthe known character. The system may recommend all known characters thathave a distance from the known character that is below a certainthreshold. Alternatively, or in addition, the system may recommend Xnumber of closest characters, where X is a threshold set by a user ordetermined by the system. Alternatively, or in addition, the system mayrecommend a ranked list based on level of relevancy or distance.

Developing Character Models

The character models 116-128 illustrated in FIG. 1 provide an examplefor a single television show. To develop character models for numerouscharacters spanning a large variety of media content, automatedtechniques, partially automated techniques, manual techniques, and theircombinations are employed. Several techniques are discussed below, whichmay be used independently or in combination.

Semantic analysis of text may be used to develop character models. Textassociated with a character is identified across different text-basedmedia, such as Internet websites. Terms associated with the characterare aggregated from the text. Semantic analysis techniques are then usedto map the character onto the desired feature space. For example, acharacter model for Penny 106 may be developed using semantic analysisby identifying text associated with Penny 106. For example, text may beidentified with a character when it is a certain number of words or lessaway from the character's name or image. Various terms, such as“engineer,” “science,” or “analytical,” are aggregated from theidentified text. These terms are mapped to the appropriate attribute ofthe character. In this case, the appropriate attribute is “scientist.”In one example, each time a term maps to an attribute of the character,that character's attribute value increases by a determined amount—suchas one. Similarly, when a term maps to the negative of an attribute ofthe character, such as “dislikes math,” that character's attribute valuedecreases by a determined amount—such as one. In either case, thedetermined amount for increasing or decreasing the attribute value maybe based on a strength value of the term. The term “engineer” may have astrength value of 0.25 while the term “gorgeous” has a strength value of1.0. Similarly, “incredibly gorgeous” may have a strength value of 1.5.The mapping and strength values may be stored in a database for easyaccess when developing the character models.

Potential sources of the terms that describe the character include thecharacter's official webpage, Wikipedia pages for the character and showthe character appears in, fan pages, social networking pages, socialnetworking chatter (e.g., tweets from Twitter, Facebook comments, etc.),and other Internet sources.

Aggregating users' responses to a character may also be used to developcharacter models. For example, responses related to a character'sattribute may be determined as “positive” or “negative” and used toincrease or decrease the attribute value in the character modelaccordingly. Users' responses may be aggregated from across theInternet, such as social networks, webpages, emails, and the like.Additionally, character models may be based on explicit thumbs up anddown by users, clustering user preferences for characters with other webpages the user likes and/or Internet groups of which the user is a part,the expertise of web pages and Internet groups that mention thecharacter, Nielsen ratings for a show, awards, trade magazines, expertcommentary, and editorial reviews.

Survey methodologies may be used to develop character models. A surveycan be conducted to assess a population's opinion about a character'sattributes. The surveys may ask several questions to get the underlyingvalue for a more subtle attribute. For example, to assess a “socialcompetence” attribute, respondents may be asked if the character has alot of friends, if the character is familiar with popular culture, andif the character is able to adapt to both formal and informalsituations.

These surveys may be, for example, full-length surveys looking at eachrespondent's overall reaction to a character or microsurveys askingrespondents single, discrete questions using services such as MechanicalTurk.

Expert validation may be used to develop character models. Certainattributes, such as “agency” or “moral character,” may benefit frominput from experts in various fields including media studies andpsychology. For these attributes, survey methodologies may be combinedto populate the majority of the database, with expert validation on arandomly selected subset to ensure methodologies used to populate themajority of the database are in line with best practices from thosefields.

User feedback may be used to develop character models. Users' responsesto characters may be aggregated and used to feed into the database ofcharacter models. For example, when a consumer endorses via socialnetworks, shares with friends, or watches a given character in a mediacontent, the consumer is prompted to provide feedback on why they liked,shared, or viewed that particular character or media content.

Character Preference Function

Thus far, the described techniques for determining distance have notdifferentiated between the importance of the various characterattributes as viewed from the perspective of a consumer. To be moreprecise, we have defined a single distance function applicable to anycharacter. These search techniques can be further refined by taking intoaccount whether a consumer cares more about similarity along somedimensions of the character model than other dimensions of the charactermodel. This preference information about the consumer is captured in acharacter preference function and is used for determining preferencesand distances between characters.

Different consumers may have different character preference functions,which are each based on the associated consumer's preferences. Forexample, Jessica, a television viewer, may care only about the gender ofcharacters and the attractiveness of characters. In particular, shelikes attractive characters and female characters. These preferences maybe gathered directly or indirectly. For example, a user may directlyinput their preferences or the user's preferences may be learned byidentifying which characters the user likes. As alluded to above, theseuser-specific preferences can be encoded in a set of “weights” β_(i) foreach attribute. Here, Jessica's character preference function isrepresented as:f(jessica)=β₁ ·c ₁+0·c ₂+β₃ ·c ₃+0·c ₄where β₁, β₃ are both greater than 0. In this example, the coefficientson the second and fourth attributes (i.e., coefficient to c₂ scientistattribute and coefficient to c₄ friendliness attribute) are 0 becauseJessica does not care about them, and the coefficients on the attributesshe likes (i.e., β₁ coefficient to c₁ gender attribute and β₃coefficient to c₃ attractiveness attribute) are positive. If Jessicapreferred male characters rather than female characters, the β₁coefficient for the gender attribute would be negative. Thesecoefficients may be referred to as the consumer's preferencecoefficients and they correlate to all or some of the values of thecharacter models. The preference coefficients may be integers or realnumbers. Negative preferences (e.g., a dislike for an attribute) may beincorporated into a preference function as well. One of ordinary skillin the art will appreciate that coefficients are a type of parameter,and that more generalized parameters for other functional forms may beused instead of coefficients.

In another example, George, another television viewer, is interestedonly in the scientist dimension of characters. George likes scientistsregardless of their other attributes. George's character preferencefunction is significantly simpler than Jessica's character preferencefunction because George only cares about one dimension—the scientistdimension. Thus, the coefficients, or weights, on all the otherdimensions are 0. George's character preference function is reduced to:f(george)=β₂ ·c ₂where β₂ is greater than 0. Jessica's and George's preferences arecaptured in their character preference functions. These characterpreference functions can be used to recommend characters and todetermine distances between characters, with both recommendations anddistances being individualized for the consumer associated with thecharacter preference function.

As discussed above, the system can recommend characters based on thecharacter preference function. Using Jessica's character preferencefunction and the character models illustrated in FIG. 1, the system willrank Penny 106 and Bernadette 112 highly. This system will recommendPenny 106 and Bernadette 112 to Jessica because Penny 106 and Bernadette112 are ranked highly based on a combination of Penny's character model120, Bernadette's character model 126, and Jessica's characterpreference function. The recommendation values that are translated intorankings can be calculated using the consumer's character preferencefunction:f(consumer,character)={right arrow over (β)}·{right arrow over (c)}where β represents the preference coefficients of the consumer along nattributes and c represents a character's attributes, such as from acharacter model, along the same n attributes. This character preferencefunction may be computed multiple times for different characters todetermine the distance between characters for the particular consumer.

Based on George's character preference function and the character modelsillustrated in FIG. 1, the system will rank all characters that arescientists equally highly and recommend them to George. Similarequations can be used to calculate recommendation values for George.

The character preference function may also represent different oradditional information than information capturing what the consumerlikes or dislikes. For example, Jessica's character preference functionmay represent what Jessica likes, what type of characters or contentJessica has viewed in the past, what character or content Jessica hasprovided feedback on, whether the feedback has been positive/negative, acombination of one or more of these elements, or the like.

The system can also determine distances between characters by using thecharacter preference functions in combination with the character models.For example, based on Jessica's character preference function describedabove and the character models illustrated in FIG. 1, the system willidentify Penny and Bernadette as having a relatively low distancebecause Penny and Bernadette share the same gender (female) andattractiveness (attractive). Recall that gender and attractiveness arethe two dimensions that are relevant in Jessica's character preferencefunction. If gender and attractiveness are the only dimensions relevantto Jessica's character preference function, the distance between Pennyand Bernadette is 0. Similarly, the system will determine that Howardand Penny have a relatively high distance because Howard and Pennydiffer in both gender and attractiveness. In particular, Howard is aunattractive and male while Penny is attractive and female. Again,recall that Jessica's character preference function emphasizes thegender and attractiveness dimensions. Thus, Howard and Penny may beviewed by the system as opposites with respect to Jessica's characterpreference function because Howard and Penny differ in both gender andattractiveness.

Second Order Terms for Character Preference Functions

In some instances, the distances between characters based on charactermodels and character preference functions may be computed using secondorder or higher terms. For example, a consumer may like male scientistsbut may dislike female scientists. Similarly, a consumer may likeattractive females as well as attractive scientists. To distinguishamong these combinations, second order or higher preferences need to becaptured in the character preference function. With regard to secondorder terms, note that, for example, a preference for an attractivefemale character (second order) is different than a preference for bothattractive characters (first order) and female characters (first order).

In one example, a consumer named Brian likes female scientist characters(second order), and attractive characters (first order), and friendlycharacters (first order). Note that a preference for a female scientistis different than a preference for female characters and characters thatare scientists. Brian's character preference is:f(c)=β₁ ·c ₁+β₂ ·c ₂+β₃ ·c ₃+β₄ ·c ₄+γ_(1,2) ·c ₁ ·c ₂+γ_(3,4) ·c ₃ ·c ₄where β₁, β₂, β₃, β₄, γ_(1,2), γ_(3,4)>0. The second order termscaptured by the positive coefficients γ_(1,2), γ_(3,4) provide moreprecise metrics for character recommendations and distances betweencharacters with relation to Brian's preferences. Brian's characterpreference is described below with annotations for clarity:f(c)=β₁ ·c ₁[gender]+β₂ ·c ₂[scientist]+β₃ ·c ₃[attractiveness]+β₄ ·c₄[friendliness]+γ_(1,2) ·c ₁[gender]·c ₂[scientist]+γ_(3,4) ·c₃[attractiveness]·c ₄[friendliness]

This second order of preference is captured in Brian's characterpreference function to provide more precise metrics for characterrecommendations and distances between characters with relation toBrian's preferences.

In particular, Brian's character preference function illustrates thatBrian likes female scientists. This is stored in Brian's characterpreference function using the vector of weights {right arrow over (β)}and the matrix of weights for the second order terms γ. Thus, whencomputing recommendations and distances using Brian's characterpreference function, the system can take Brian's second orderpreferences into consideration. In this example with relation to Brian'scharacter preference function, the distance d between a first characterA and a second character B is determined as follows:

${d( {A,B} )} = {{\beta \cdot {{abs}( {A - B} )}^{\prime}} + {{{abs}( {A - B} )}^{\prime} \cdot \frac{\gamma}{2} \cdot {{abs}( {A - B} )}}}$

One of ordinary skill will readily appreciate that additional techniquesmay be used to represent the character preference functions.

Preference Models

A preference model may be developed by using data from multiplecharacter preferences function in conjunction with known attributesabout the users associated with the character preference functions. Adatabase of users is aggregated that associates users with one or moreattributes and their character preference function. Using this database,a preference model can be determined for a person or a group of people.

For example, assume that 75% of users who are both female and have adegree in a science, technology, engineering, or mathematics (STEM)field have user profiles that indicate they enjoy watching femalescientists in media. This is a strong indicator that other females witha degree in a STEM field will also enjoy watching female scientists inmedia. Thus, when a new user joins the system who provides their genderas female and education as related to STEM, the system can predict thatthe new user will enjoy watching female scientists without requiringdirect feedback from the new user about her viewing preferences.Accordingly, the system can recommend media using the techniquesdescribed above by using the prediction that the new user enjoys femalescientists.

Similarly, multiple character preference functions can be used topredict what characteristics a particular demographic will enjoy. Forexample, if a group viewing is being conducted (such as in a movietheater), statistics about the attributes of the group members can begathered in advance. The statistics about the group's attributes can beused to identify the types of characters the group is likely to enjoy.Using the techniques described above, media can be identified that thegroup is likely to enjoy. The preference model can also be extendedbeyond media to any type of character.

Beyond relying on demographic information, user specific preferencefunctions can be calculated in a number of different ways: 1) Directelicitation: asking users about their preferences for specificcharacters, character attributes, or combinations of attributes. 2)Inference from favorite characters and shows. For example, given a setof characters that the user likes and a set that the user does not like,one could estimate the preference weights by assuming that theprobability that the user “liked” a character was a sigmoid function

$( {{e.g.},\frac{1}{1 + {\exp( {- x} )}}} )$of the user's character preference function. By finding the coefficientsβ, γ, that maximized the joint probability that the user likes and didnot like those sets of characters, the system can calculate the user'scharacter preference function. 3) Inference from physiologicalrecording: In the absence of direct reporting from the consumer abouttheir character preferences, eye-tracking, facial responses, posturemapping or any number of other types of physiological recording may beused to detect which characters demand the most attention from aconsumer, and whether that attention is positive or negative. Giventhese physiological responses to characters, a preference model could beinferred following a similar method as that described for 2). Thepreference model can also be extended beyond media to any type ofcharacter.Content Recommendation

Information about characters can be used to determine ratings andrecommendations of media content and to determine distances betweenmedia content. For example, a consumer who likes attractive scientistswould likely enjoy a show that employs multiple characters that areattractive scientists. The same consumer would likely not enjoy a showthat primarily employs characters that are unattractive non-scientists.

Media content may be rated using a salience weighted sum of a consumer'spreferences for all or some characters included in the media content.The relative salience of the character in media content can bedetermined multiple ways.

One method to determine salience is to base the salience on thepercentage of screen time the character gets in relation to the totalscreen time of all characters. For a simple example, consider a comedyshow that includes a doctor, an engineer, and an attorney as characters.The doctor is on screen for a total of 1,100 seconds, the engineer is onscreen for a total of 1,500 seconds, and the attorney is on screen foronly 600 seconds. Using this first method, the salience S of a character(Char) in relation to all the characters (AllChars) can be computed as:

${S({Char})} = \frac{{ScreenTime}({Char})}{{ScreenTime}({AllChars})}$

In this particular example of the doctor, engineer, and attorney, thesalience for each character is computed as follows:

${S({doctor})} = {\frac{1\text{,}100}{{1\text{,}000} + {1\text{,}500} + 600} = 0.34375}$${S({engineer})} = {\frac{1\text{,}500}{{1\text{,}100} + {1\text{,}500} + 600} = 0.46875}$${S({attorney})} = {\frac{600}{{1\text{,}100} + {1\text{,}500} + 600} = 0.1875}$

Another method to determine salience is to base the salience on thenumber of reactions detected in social media relating to a character.For example, Twitter, Facebook, Google+, Instagram, and other socialnetworking websites may be monitored to track the number of times acharacter's name is mentioned, a character's image is published, acharacter's reference is acknowledged (e.g., liking a character's fanpage on Facebook), and the like. Using this method, the relativesalience of a character can be determined based on the number of times acharacter in a media content elicits reactions versus the number oftimes all characters in the media content elicit reactions. For example,this computation can be performed as:

${S({Char})} = \frac{{NumberOfReactions}({Char})}{{NumberOfReactions}({AllChars})}$

Yet another method to determine salience is to consider the character'sprevalence on the Internet in general. The prevalence can be determineda number of ways. On method is to identify the number of search resultsreturned from a reliable search engine for the name of a character. Forexample, searching Google for “Bill Clinton” returns about 40,400,000results. Searching Google for “George W. Bush” returns about 95,800,000results. Thus, the character George W. Bush is more salient with respectto the character Bill Clinton. Using this method, the prevalence of thecharacter on the Internet can be used to calculate salience in a similarmanner as the number of on-screen minutes for characters, as describedabove.

Another method for gathering either general or user specific charactersalience is to utilize physiological responses to characters. Forexample, eye-tracking data may be used to assess that on average viewersspend more time looking at Sheldon, Leonard, and Penny than any othercharacters on The Big Bang Theory, giving these characters particularlyhigh salience over the population. Alternatively, or in addition, thesystem may compute that a specific user, Jessica, spent the majority ofher time looking at Penny, indicating that Penny was the most salientcharacter to her. An additional way to gather user specific salience isby analyzing a user's behavior on social media in response to watching ashow.

One of ordinary skill in the art will readily recognize that not allcharacters of the media content must be considered for the saliencetechniques described above. For example, a minimum threshold value maybe set so that insignificant characters (e.g., those who receive verylittle screen time, those who elicit very few social media reactions,those who have low prevalence on the Internet, and the like) are notconsidered in the salience calculations. Alternatively, or in addition,a maximum threshold may also be set so that characters in a particularmedia content that are very popular do not overshadow other charactersin the salience calculations.

The consumers' preferences, the characters' attributes, and thecharacters' salience are considered for calculating a rating for a mediacontent. This rating can then be used to rank various media content andrecommend media content to consumers. Consider a consumer named Stevenwho is interested in viewing more females in television shows. Stevenhas indicated, or it has been inferred from his revealed preferences,that he would particularly like to see female scientists and that heprefers scientists in the television shows that he watches to beattractive. The system computes a character preference function for eachcharacter with relation to Steven's preferences to account for theseattributes. The character preference function represents a consumer'srating of the character. To compute the character preference functions,the salience of the characters is used. In this case, the salience foreach character is pre-computed and identified in Table 1, below, withrelation to the characters identified in FIG. 1. The salience values inthis case were calculated based on screen time for each character usinga particular episode of the show The Big Bang Theory. As discussedabove, other methods may be used. Additionally, the calculation may bebased on a single scene of a media content, a single episode of a mediacontent, a single season of a media content, or all the available showsof a media content.

TABLE 1 Exemplary Salience Values Character Salience Sheldon 0.2 Leonard0.2 Penny 0.2 Howard 0.15 Rajesh 0.15 Bernadette 0.05 Amy 0.05

Let cε

^(n) represent the attribute values for a character char on N distinctdimensions. The following character preference function is used tocalculate a rating for each character:

${f({Char})} = {{\sum\limits_{i = 1}^{N}{\beta_{i} \cdot c_{i}}} + {\sum\limits_{i,j}{\beta_{i,j} \cdot c_{i} \cdot c_{j}}}}$

Additionally, higher order terms may also be included. For example, thecharacter preference function can be extended to:

${f({char})} = {{\sum\limits_{i = 1}^{N}{\beta_{i} \cdot c_{i}}} + {\sum\limits_{i,j}{\beta_{i,j} \cdot c_{i} \cdot c_{j}}} + {\sum\limits_{i,j,k}{\beta_{i,j,k} \cdot c_{i} \cdot c_{j} \cdot c_{k}}} + {{higher}\mspace{14mu}{order}\mspace{14mu}{terms}}}$

The coefficients β are determined separately for each user to allow forpersonalized recommendations. For the preferences indicated by Steven,the following character preference function is used to calculate arating for each character:f(Char)=Gender+(Gender*Scientist)+(Attractiveness*Scientist)

Using the character models of FIG. 1 and Steven's character preferencefunction, the ratings of the characters illustrated in FIG. 1 arecomputed for Steven as follows:f(Sheldon)=−1+(−1×1)+(−1×1)=−3f(Leonard)=−1+(−1×1)+(0×1)=−2f(Penny)=1+(1×−1)+(1×−1)=−1f(Howard)=−1+(−1×1)+(−1×1)=−3f(Rajesh)=−1+(−1×1)+(0×1)=−1f(Bernadette)=1+(1×1)+(1×1)=3f(Amy)=1+(1×1)+(−1×1)=1

These calculated character ratings are valid for the charactersidentified for a particular episode of The Big Bang Theory. Thecalculated character ratings and their corresponding salience values canbe used to calculate a show rating for that particular episode of TheBig Bang Theory. The calculation of the show rating is performed bysumming the product of each character's salience and rating. Using asalience vector s and characters rating vector {right arrow over (R)},an episode rating r is calculated as:R(Show_EpisodeX)={right arrow over (S)}·{right arrow over (R)}

In this particular example of Steven with relation to the charactersillustrated in FIG. 1, the rating R of The Big Bang Theory (TBBT)episode is calculated as:R(TBBT)=(0.2×−3)+(0.2×−2)+(0.2×−1)+(0.15×−3)+(0.15×−1)+(0.05×3)+(0.05×1)=−1.6Thus, the rating for this particular episode of The Big Bang Theory forthe consumer Steven is −1.6. For recommendations, this rating value iscompared to similarly calculated rating values for other shows. Rankingsare prepared based on the rating values. For example, the media contentwith the highest rating values will be ranked highest while the mediacontent with the lowest rating values will be ranked the lowest. Thehighest ranked shows are recommended to the consumer, as these highlyranked shows represent the shows that the consumer is likely to beinterested in or is likely to enjoy. One of ordinary skill in the artwill appreciate that coefficients are a type of parameter, and that moregeneralized parameters for other functional forms may be used instead ofcoefficients.System Integration

The techniques discussed above may be used separately or combined toproduce a powerful system for discovering and organizing characters andmedia content based on consumer preferences.

FIG. 2 illustrates an exemplary block diagram for a combined techniqueto perform discovery and organization of characters and media content.At block 202, the system accesses feedback from users. This feedback isused to determine the user's preferences and develop a characterpreference function. The feedback may be explicit, such as throughdirect questions. The feedback may be implicit, such as by analyzingwebpages the user has clicked on, viewed, commented on, shared, and thelike. The feedback may be physiological, such as by eye tracking,galvanic skin response, electroencephalography, facial expressiontracking, posture mapping, and the like. The character preferencefunction is stored in a database and associated with the user from whomthe feedback was received.

At block 204, attributes to be included in a character model aredetermined. Multiple examples are described. Physical attributes of thecharacters may be tracked, such as gender, age, and the like.Personality attributes may be tracked, such as kindness, humor, cruelty,and the like. Social attributes or roles may be tracked, such asrelationship (parent/grandparent), community leader, occupation, and thelike. Additional attributes may be tracked, such as race, socioeconomicclass, and the like.

At block 206, data relevant to the characters and their attributes areextracted from data sources. The data may be extracted from the text ofwebpages, such as Wikipedia, fan sites, social networks, such asFacebook and Twitter, surveys, expert validation, and other sources.

At block 208, character decomposition is performed. The data extractedfrom the data sources is used to assign values for each attributeidentified in block 204 for the characters in a character modeldatabase. For example, the various techniques described above may beused to assign values for each character's character model.

At block 210, the user preference model and the character model areaccessed to determine user preferences across the character attributesof the character model. The preference data is used to discover newcharacters or new shows that the user may like. The preference data mayalso be used to organize characters or shows based on the user'spreferences, such as identifying which characters are similar ordissimilar. As a result, the system is able to efficiently and reliablyrecommend characters and media content to the user.

FIG. 3 illustrates an exemplary process for recommending media. At block302, a recommendation system accesses a set of salience values. Thesalience values of the set are associated with a media content. Eachsalience value is associated with one character from the media content.The salience values are indicative of how important the characters areto the feel or tone of the show. The higher the salience value of acharacter, the more important the character. At block 304, the systemaccesses a character preference function. The character preferencefunction is associated with a user of the system. The characterpreference function comprises information that identifies a plurality ofpreference coefficients. Each of the preference coefficients in theplurality of preference coefficients is associated with at least oneattribute of interest, selected from a plurality of attributes. Forexample, the preference function may indicate that the user has apreference coefficient of 1 associated with a “gender” attribute ofinterest and a preference coefficient of 1 associated with a “scientist”attribute of interest.

At block 306, the system accesses a first character model. The firstcharacter model is associated with a first character from the mediacontent. The first character model includes information that identifiesa first set of attribute values. The attribute values are matched withattributes of the first character. The attributes may be the same as theattributes for which the character preference function includespreference coefficients. The first character is also associated with afirst salience value from the set of salience values. The first saliencevalue will be used to determine how much influence the first characterhas when computing a rating of the media content.

At block 308, the system accesses a second character model. The secondcharacter model is associated with a second character from the mediacontent. The second character model includes information that identifiesa second set of attribute values. The attribute values are matched withattributes of the second character. The attributes may be the same asthe attributes for which the character preference function includespreference coefficients. The second character is also associated with asecond salience value from the set of salience values. The secondsalience value will be used to determine how much influence the secondcharacter has when computing a rating of the media content.

At block 310, the system calculates a first character rating of thefirst character by performing a summation of the products of theplurality of preference coefficients with the first set of attributevalues. For example, the system will multiply the preference coefficientfor gender with the first character's attribute value for gender. Thesystem will also multiply the preference coefficient for scientist withthe first character's attribute value for scientist. These two productsfor gender and scientist are then added together. The first characterrating of the first character is based on this summation.

At block 312, the system similarly calculates a second character ratingof the second character by performing a summation of the products of theplurality of preference coefficients with the second set of attributevalues. For example, the system will multiply the preference coefficientfor gender with the second character's attribute value for gender. Thesystem will also multiply the preference coefficient for scientist withthe second character's attribute value for scientist. These two productsfor gender and scientist are then added together. The second characterrating of the second character is based on this summation.

At block 314, the system calculates a media content rating. The mediacontent rating is calculated based on the first salience value, secondsalience value, the first character rating, and the second characterrating. The salience values are used to weight the influence that eachcharacter rating has on the media content rating.

At block 316, the system recommends the media content to the user basedon the media content rating. The recommendation may be simply providingthe title of the media content, providing a link to the media content,displaying the media content, and the like. For example, the mediacontent may be an advertisement that the system has determined the usermay enjoy, connect with, or sympathize with. In other examples, themedia content may be a written article, a game, a mobile app or computerapplication, and the like.

In general, the blocks of FIG. 3 may be performed in various orders, andin some instances may be performed partially or fully in parallel.Additionally, not all blocks must be performed. For example, the set ofsalience values need not necessarily be accessed before accessing thefirst and second character models.

FIG. 4 illustrates an exemplary process for recommending media. At block402, a recommendation system calculates a first salience value of a setof salience values. The first salience value is associated with a firstcharacter of a plurality of characters of a media content. The firstsalience value is calculated based on the on-screen time of the firstcharacter in the media content. More specifically, the system determinesor accesses a total on-screen time value. For example, the totalon-screen time value may be sum of the time all (or select) charactersof the media content spend on screen. The system also determines oraccesses the on-screen time for the first character. The first saliencevalue is calculated by dividing the on-screen time of the firstcharacter by the total on-screen time value.

At block 404, the system calculates a second salience value of the setof salience values. The second salience value is associated with asecond character of the plurality of characters of the media content.The second salience value is calculated based on the on-screen time ofthe second character in the media content. More specifically, the systemdetermines or accesses the on-screen time for the second character. Thesecond salience value is calculated by dividing the on-screen time ofthe second character by the total on-screen time value.

The salience values of the set are associated with the media content.Each salience value is associated with one character from the mediacontent. The salience values are indicative of how important thecharacters are to the feel or tone of the show. The higher the saliencevalue of a character, the more important the character.

At block 406, the system accesses a character preference function. Thecharacter preference function is associated with a user of the system.The character preference function comprises information that identifiesa plurality of preference coefficients. Each of the preferencecoefficients in the plurality of preference coefficients is associatedwith at least one attribute of interest, selected from a plurality ofattributes. For example, the preference function may indicate that theuser has a preference coefficient of 0.8 associated with “femalescientist” attributes of interest, a preference coefficient of 1associated with a “female” attribute of interest, and a preferencecoefficient of 1 associated with a “scientist” attribute of interest.

This character preference function is a second order function. Thesecond order function has first order terms and second order terms. Thecharacter preference function associates at least one of the pluralityof preference coefficients with two or more attributes of interest ofthe plurality of attributes. In this example, the character preferencefunction associates the preference coefficient of 0.8 with theattributes of interest of “female scientist.”

At block 408, the system determines a first character model. The firstcharacter model is associated with the first character from the mediacontent. The first character model includes information that identifiesa first set of attribute values. The attribute values are matched withattributes of the first character. The attributes associated with theattribute values may be the same as the attributes for which thecharacter preference function includes preference coefficients.

The first character model is determined in part by identifying textualcontent associated with the first character in electronic sources, suchas websites, electronic books, electronic newspapers and magazines,social media, and the like. The system aggregates a plurality ofattribute terms associated with the first character from the textualcontent. For example, the system may aggregate terms such as “cute,”“smart,” “social,” and the like. The system maps at least some of theplurality of attribute terms to at least some of the plurality ofattributes. This mapping allows a relationship to be identified betweenthe aggregated terms (such as “cute”) and the attributes of thecharacter that are tracked (such as “attractive”). The system updatesthe attribute values of the first character based on the plurality ofattribute terms.

At block 410, the system calculates a first character rating of thefirst character. The system sums the first order terms and the secondorder terms of the character preference function in conjunction with thefirst character model. For the first order terms, the system calculatesthe products of the plurality of preference coefficients that are firstorder with the first set of attribute values. In this example, thesystem multiplies the preference coefficient of 1 associated with“scientist” with the first character model's attribute value for“scientist.” Similarly, the system multiplies the preference coefficientof 1 associated with “female” with the first character model's attributevalue for “female.” For the second order terms, the system determinesthe product of the at least one of the plurality of preferencecoefficients with each attribute value of the first set of attributevalues of the two or more attributes of interest of the plurality ofattributes. In order words, the system calculates the products of theplurality of preference coefficients that are second order with thefirst set of attribute values. In this example, the system multipliesthe preference coefficient of 0.8 associated with “female scientist”with the first character model's attribute value for “female” and withthe first character model's attribute value for “scientist.” The firstorder terms and second order terms are then summed to produce the firstcharacter rating.

Each of the attribute terms may be associated with a strength value.This is helpful for distinguishing between strong terms and less strongterms. For example, a strong term may indicate that a character is“definitely friendly.” A less strong term may indicate that thecharacter is “sometimes friendly.” The system then updates the attributevalues of the first character based on the corresponding strength valuesof the attribute terms. In this example, “definitely friendly” may beassociated with a 1.5 for the friendliness attribute, while “sometimesfriendly” is associated with a 0.75 for the friendliness attribute. Inone example, the system stores the updated attribute values of the firstcharacter in a database as a vector, the vector associated with thefirst character.

At block 412, the system determines a second character model. The secondcharacter model is associated with the second character from the mediacontent. The second character model includes information that identifiesa second set of attribute values. The attribute values are matched withattributes of the second character. The attributes associated with theattribute values may be the same as the attributes for which thecharacter preference function includes preference coefficients.

The second character model is determined in a similar fashion asdescribed above with respect to the first character model. The secondcharacter model is determined in part by identifying textual contentassociated with the second character in electronic sources. The systemaggregates a plurality of attribute terms associated with the secondcharacter from the textual content. The system maps at least some of theplurality of attribute terms to at least some of the plurality ofattributes. The system updates the attribute values of the firstcharacter based on the plurality of attribute terms and thecorresponding strength values of the attribute terms. In one example,the system stores the updated attribute values of the second characterin a database as a vector, the vector associated with the secondcharacter.

At block 414, the system calculates a second character rating of thesecond character. The second character rating is computed in a similarfashion as the first character rating. However, the second charactermodel and second character attribute values are used. At block 416, thesystem calculates a second character rating of the second character in asimilar fashion as calculated for the first character.

At block 416, the system calculates a media content rating. The mediacontent rating is calculated based on the first salience value, secondsalience value, the first character rating, and the second characterrating. The salience values are used to weight the influence that eachcharacter rating has on the media content rating.

At block 418, the system accesses a minimum content rating value. Atblock 420, the system compares the media content rating to the minimumcontent rating value. The media content rating is numerical and theminimum content rating value is numerical. If the media content ratingis greater than the minimum content rating value, the system moves toblock 422. Otherwise, the process ends at block 424.

At block 422, the system recommends the media content to the user basedon the media content rating. The recommendation may be simply providingthe title of the media content, providing a link to the media content,displaying the media content, and the like. For example, the mediacontent may be an advertisement that the system has determined the usermay enjoy, connect with, or sympathize with. In other examples, themedia content may be a written article, a game, a mobile app or computerapplication, and the like.

In general, the blocks of FIG. 4 may be performed in various orders, andin some instances may be performed partially or fully in parallel.Additionally, not all blocks must be performed. For example, the firstcharacter rating and the second character rating may be computed inparallel.

While FIG. 4 and FIG. 5 were described with respect to recommendingmedia to a user, the techniques described above may be applied tovarious other systems. In one example, the system may be used to providean interface to filter and curate information based on characterdecomposition. More specifically, the system may be used to: alterrankings for a list of items, control viewing abilities of a televisionor other viewing system, suggest content for purchasing, recommend videogames or prevent access to video games, filter content to preventchildren from viewing content with a negative message, characterize whata child is watching, or find the intersection between the desires of aparent and the viewing preferences of a child.

In another example, the system may provide information to contentproducers to understand audience preferences based on characterdecomposition. More specifically, the system may be used to: aggregateinsights to content producers on what types of characters to createbased on aggregated user demand or preferences, or identifycharacteristics/attributes of a character most likely to resonate with aparticular target user group to enable mapping of a character/celebritywith a target audience.

In another example, the system may provide complementary, simultaneous,character-based browsing and information discovery. More specifically,the system may be used to: provide a second screen experience, enhanceviewing experience of media with simultaneous recommendations, andprovide in-play ads based on characters appearing in the show.

In yet another example, the system may be used for user-generatedcharacter creation. Users can create their own characters based on whatfeatures the user likes. This allows for the collection ofuser-generated signals and data that informs the development ofcharacters based on attributes the user (or users) value most. This alsogenerates insights on user preferences and latent demand for specifictypes of characters as well as explicitly informing and directing newcharacter development for content providers.

FIG. 5 depicts an exemplary computing system 500 configured to performany one of the above-described processes. In this context, computingsystem 500 may include, for example, a processor, memory, storage, andinput/output devices (e.g., monitor, keyboard, touch screen, disk drive,Internet connection, etc.). However, computing system 500 may includecircuitry or other specialized hardware for carrying out some or allaspects of the processes. In some operational settings, computing system500 may be configured as a system that includes one or more units, eachof which is configured to carry out some aspects of the processes eitherin software, hardware, or some combination thereof.

FIG. 5 depicts computing system 500 with a number of components that maybe used to perform the above-described processes. The main system 502includes a motherboard 504 having an input/output (“I/O”) section 506,one or more central processing units (“CPU”) 508, and a memory section510, which may have a flash memory device 512 related to it. The I/Osection 506 is connected to a display 524, a keyboard 514, a diskstorage unit 516, and a media drive unit 518. The media drive unit 518can read/write a computer-readable medium 520, which can containprograms 522 and/or data. The I/O section 506 may also connect to cloudstorage using, for example, cellular data communications or wirelesslocal area network communications.

At least some values based on the results of the above-describedprocesses can be saved for subsequent use. Additionally, anon-transitory computer-readable medium can be used to store (e.g.,tangibly embody) one or more computer programs for performing any one ofthe above-described processes by means of a computer. The computerprogram may be written, for example, in a general-purpose programminglanguage (e.g., Perl, C, C++, Java) or some specializedapplication-specific language.

Although only certain exemplary embodiments have been described indetail above, those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiments withoutmaterially departing from the novel teachings and advantages of thisinvention. For example, aspects of embodiments disclosed above can becombined in other combinations to form additional embodiments.Accordingly, all such modifications are intended to be included withinthe scope of this invention.

What is claimed is:
 1. A computer-implemented method of recommendingmedia content, the method comprising: accessing a plurality ofpreference coefficients, each preference coefficient of the plurality ofpreference coefficients associated with at least one attribute ofinterest of a plurality of attributes; accessing a first plurality ofattribute values for the plurality of attributes of a first character ofa plurality of characters of a media content; accessing a secondplurality of attribute values for the plurality of attributes of asecond character of the plurality of characters; calculating a firstcharacter rating of the first character based on the plurality ofpreference coefficients and the first plurality of attribute values;calculating a second character rating of the second character based onthe plurality of preference coefficients and the second plurality ofattribute values; calculating a media content rating, the media contentrating calculated based on the first character rating and the secondcharacter rating; and selecting, based on the media content rating, themedia content from among a plurality of media contents.
 2. Thecomputer-implemented method of claim 1, further comprising: determiningthe first plurality of attribute values in part by: identifying textualcontent associated with the first character, the textual contentcontained in an electronic source; aggregating a plurality of attributeterms associated with the first character from the textual content;mapping at least some of the plurality of attribute terms to at leastsome of the plurality of attributes; and updating attribute values ofthe first character based on the plurality of attribute terms.
 3. Thecomputer-implemented method of claim 2, wherein each of the at leastsome of the plurality of attribute terms is associated with acorresponding strength value; and updating attribute values of the firstcharacter is based on the corresponding strength values.
 4. Thecomputer-implemented method of claim 2, further comprising: storing theupdated attribute values of the first character in a database as avector, the vector associated with the first character.
 5. Thecomputer-implemented method of claim 1, further comprising: accessing aminimum content rating value; comparing the media content rating to theminimum content rating value, wherein the media content rating isnumerical and the minimum content rating value is numerical; anddetermining the media content rating is greater than the minimum contentrating value.
 6. The computer-implemented method of claim 1, wherein: acharacter preference function comprises the plurality of preferencecoefficients; the character preference function is a second orderfunction associating at least one of the plurality of preferencecoefficients with two or more attributes of interest of the plurality ofattributes; calculating the first character rating of the firstcharacter further comprises determining the product of multiplying theat least one of the plurality of preference coefficients with eachattribute value of the first plurality of attribute values of the two ormore attributes of interest of the plurality of attributes; andcalculating the second character rating of the second character furthercomprises determining the product of multiplying the at least one of theplurality of preference coefficients with each attribute value of thesecond plurality of attribute values of the two or more attributes ofinterest of the plurality of attributes.
 7. A non-transitorycomputer-readable storage medium comprising computer-executableinstructions for recommending media content, the computer-executableinstructions comprising instructions for: accessing a plurality ofpreference coefficients, each preference coefficient of the plurality ofpreference coefficients associated with at least one attribute ofinterest of a plurality of attributes; accessing a first plurality ofattribute values for the plurality of attributes of a first character ofa plurality of characters of a media content; accessing a secondplurality of attribute values for the plurality of attributes of asecond character of the plurality of characters; calculating a firstcharacter rating of the first character based on the plurality ofpreference coefficients and the first plurality of attribute values;calculating a second character rating of the second character based onthe plurality of preference coefficients and the second plurality ofattribute values; calculating a media content rating, the media contentrating calculated based on the first character rating and the secondcharacter rating; and selecting, based on the media content rating, themedia content.
 8. The non-transitory computer-readable storage medium ofclaim 7, further comprising instructions for: determining the firstplurality of attribute values in part by: identifying textual contentassociated with the first character, the textual content contained in anelectronic source; aggregating a plurality of attribute terms associatedwith the first character from the textual content; mapping at least someof the plurality of attribute terms to at least some of the plurality ofattributes; and updating attribute values of the first character basedon the plurality of attribute terms.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein each of the atleast some of the plurality of attribute terms is associated with acorresponding strength value; and updating attribute values of the firstcharacter is based on the corresponding strength values.
 10. Thenon-transitory computer-readable storage medium of claim 8, furthercomprising instructions for: storing the updated attribute values of thefirst character in a database as a vector, the vector associated withthe first character.
 11. The non-transitory computer-readable storagemedium of claim 7, further comprising instructions for: accessing aminimum content rating value; comparing the media content rating to theminimum content rating value, wherein the media content rating isnumerical and the minimum content rating value is numerical; anddetermining the media content rating is greater than the minimum contentrating value.
 12. The non-transitory computer-readable storage medium ofclaim 7, wherein: a character preference function comprises theplurality of preference coefficients; the character preference functionis a second order function associating at least one of the plurality ofpreference coefficients with two or more attributes of interest of theplurality of attributes; calculating the first character rating of thefirst character further comprises determining the product of multiplyingthe at least one of the plurality of preference coefficients with eachattribute value of the first of attribute values of the two or moreattributes of interest of the plurality of attributes; and calculatingthe second character rating of the second character further comprisesdetermining the product of multiplying the at least one of the pluralityof preference coefficients with each attribute value of the second ofattribute values of the two or more attributes of interest of theplurality of attributes.
 13. An apparatus for recommending mediacontent, the apparatus comprising: a memory; and one or more computerprocessors configured to: access a plurality of preference coefficients,each preference coefficient of the plurality of preference coefficientsassociated with at least one attribute of interest of a plurality ofattributes; access a first plurality of attribute values for theplurality of attributes of a first character of a plurality ofcharacters of a media content; access a second plurality of attributevalues for the plurality of attributes of a second character of theplurality of characters; calculate a first character rating of the firstcharacter based on the plurality of preference coefficients and thefirst plurality of attribute values; calculate a second character ratingof the second character based on the plurality of preferencecoefficients and the second plurality of attribute values; calculate amedia content rating, the media content rating calculated based on thefirst character rating and the second character rating; and select,based on the media content rating, the media content.
 14. The apparatusof claim 13, the one or more computer processors further configured to:determine the first plurality of attribute values in part by:identifying textual content associated with the first character, thetextual content contained in an electronic source; aggregating aplurality of attribute terms associated with the first character fromthe textual content; mapping at least some of the plurality of attributeterms to at least some of the plurality of attributes; and updatingattribute values of the first character based on the plurality ofattribute terms.
 15. The apparatus of claim 14, wherein: each of the atleast some of the plurality of attribute terms is associated with acorresponding strength value; and updating attribute values of the firstcharacter is based on the corresponding strength values.
 16. Theapparatus of claim 14, the one or more computer processors furtherconfigured to: store the updated attribute values of the first characterin a database as a vector, the vector associated with the firstcharacter.
 17. The apparatus of claim 13, the one or more computerprocessors further configured to: access a minimum content rating value;compare the media content rating to the minimum content rating value,wherein the media content rating is numerical and the minimum contentrating value is numerical; and determine the media content rating isgreater than the minimum content rating value.
 18. The apparatus ofclaim 13, wherein: a character preference function comprises theplurality of preference coefficients; the character preference functionis a second order function associating at least one of the plurality ofpreference coefficients with two or more attributes of interest of theplurality of attributes; calculating the first character rating of thefirst character further comprises determining the product of multiplyingthe at least one of the plurality of preference coefficients with eachattribute value of the first plurality of attribute values of the two ormore attributes of interest of the plurality of attributes; andcalculating the second character rating of the second character furthercomprises determining the product of multiplying the at least one of theplurality of preference coefficients with each attribute value of thesecond plurality of attribute values of the two or more attributes ofinterest of the plurality of attributes.